Multi biometric Template Protection using Hybrid Technique

  • Authors

    • G Karthi Research Scholar
    • M Ezhilarasan Professor
    2018-09-24
    https://doi.org/10.14419/ijet.v7i4.11485
  • Template Protection, Biometric Hashing, Pin, Multimodal Biometrics, Uni-modal Biometrics and Authentication.

  • Recently, multi-biometrics system has been the important identification system for providing authentication mechanism. In this pa-per, the multi-biometric recognition system uses multiple traits (face, iris and fingerprint) for authentication. The features are extracted from the traits and feature level fusion technique is applied to the individual features traits to form a fused feature. Protection of these biometrics features against various attacks points is an important concern for authentication process. One such attack is the modification of stored template, which largely affects the performance of biometric recognition system. This paper addresses this concern, by apply-ing template protection algorithm to the biometric features. An improved hybrid template protection algorithm is proposed to protect the biometric template.The experimental results show that the proposed algorithm works better than the existing algorithms available. The proposed algorithm provides better protection to the template. Further, attacks are performed on the proposed system which provide strong resistant against the attacks.

     

  • References

    1. [1] K.Sasidhar, Vijaya L Kakulapati, Kolikipogu Ramakrishna, K. KailasaRao, “Multimodal Biometric Systems - Study to Improve Accuracy and Performanceâ€, International Journal of Computer Science & Engineering Survey (IJCSES), Vol.1, No.2, pp.54-61, November 2010.https://doi.org/10.5121/ijcses.2010.1205.

      [2] A. Rattani, D.R. Kisku, M. Bicego, M. Tistarelli, “Robust Feature-Level Multibiometrics Classificationâ€, In: Proc. of IEEE Biometric Consortium Conference, Biometrics Symposium, pp. 1-6, 2006.

      [3] A. Ross and A.K. Jain, “Information Fusion in Biometricsâ€, Pattern Recognition Letters, vol. 24, No. 13, pp. 2115-2125, 2003. https://doi.org/10.1016/S0167-8655(03)00079-5.

      [4] A. Ross and R. Govindarajan, "Feature Level Fusion Using Hand and Face Biometrics", In:Proc. of SPIE Conference on Biometric Technology for Human Identification II, Orlando, USA, pp. 196-204, March 2005. https://doi.org/10.1117/12.606093.

      [5] X. Zhou and B. Bhanu, “Feature fusion of face and Gait for Human Recognition at a distance in videoâ€, In: Proc. ofInternational Conference on Pattern Recognition, Hongkong, pp. 529-532, 2006.

      [6] Xianglei Xing, Kejun Wang, ZhuowenLv, "Fusion of Gait and Facial Features using Coupled Projections for People Identification at a Distance", Signal Processing Letters IEEE, vol. 22, pp. 2349-2353, 2015.https://doi.org/10.1109/LSP.2015.2481930.

      [7] Martin Hofmann, Stephan M. Schmidt, A N. Rajagopalan, Gerhard Rigoll, "Combined face and gait recognition using alpha matte preprocessing", In: Proc. of IAPR International Conference on Biometrics (ICB), pp. 390-395, 2012.https://doi.org/10.1109/ICB.2012.6199782.

      [8] Qi-Shen Li, Zhi-Tian Lu, Dan-Dan Zhang, "Integration of Gait and Side Face for Human Recognition in Video", Electronic Commerce and Security 2009. ISECS '09. Second International Symposium on, vol. 2, pp. 65-69, 2009.

      [9] MdWasiur Rahman, FatemaTuz Zohra, Marina L. Gavrilova, “Rank level fusion for kinect gait and face biometrie identificationâ€, In Proc. ofIEEE Symposium Series onComputational Intelligence (SSCI), pp. 1-7, 2017.

      [10] W. Yang, J. Hu and S. Wang,â€A Delaunay Triangle-Based Fuzzy Extractor for Fingerprint Authenticationâ€, In: Proc. of IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 66-70, June 2012.

      [11] Charles Herder and Ling Ren and Marten van Dijk and Meng-Day (Mandel) Yu and Srinivas Devadas, “A Stateless Cryptographically-Secure Physical Unclonable Functionâ€, IEEE Transactions on Dependable and Secure Computing, Vol. 14, No. 1, pp. 65-82, 2016.

      [12] C. Chen, C.Wang, T. Yang, D. Lin, S.Wang and J. Hu,â€Optional multi-biometric cryptosystem based on fuzzy extractor,†In: Proc of International Conference on Fuzzy Systems and Knowledge Discovery, pp. 989-994, August 2014.

      [13] M. Taniguchi, M. Shiozaki, H. Kubo and T. Fujino,â€A stable key generation from PUF responses with a Fuzzy Extractor for cryptographic authentications,†In: Proc. of IEEE second Global Conference on Consumer Electronics, pp. 525-527, October 2013.

      [14] Tee Connie, Andrew Teoh, Michael Goh, David Ngo,â€Palm Hashing: a novel approach for cancelable biometricsâ€, Information Processing Letters, vol. 93, no. 1, pp. 1-5, January 2005.https://doi.org/10.1016/j.ipl.2004.09.014.

      [15] L. Nanni, and A. Lumini,â€Random subspace for an improved BioHashing for face authentication,†Journal of Pattern Recognition Letters, vol. 29, no. 3, pp. 295- 300, February 2008.https://doi.org/10.1016/j.patrec.2007.10.005.

      [16] P. Lacharme, â€Revisiting the accuracy of the biohashing algorithm on fingerprints,†in IET Biometrics, vol. 2, no. 3, pp. 130-133, September 2013.

      [17] B. Yang and E. Martiri,â€Using Honey Templates to Augment Hash Based Biometric Template Protection,†IEEE 39th Annual Computer Software and Applications Conference, pp. 312-316 July 2015.

      [18] P. Punithavathi and S. Goethe,â€Dynamic sectored random projection for cancelable iris template,†In: Proc. of IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 711-715, September 2016.

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  • How to Cite

    Karthi, G., & Ezhilarasan, M. (2018). Multi biometric Template Protection using Hybrid Technique. International Journal of Engineering & Technology, 7(4), 2609-2613. https://doi.org/10.14419/ijet.v7i4.11485